An Expandable, Contextualized and Data-Driven Indoor Thermal Comfort Model

Abstract

Continuous discrepancies in building performance predictions creates an ongoing inclination to link contextualized, real-time inputs and users’ feedback for not only building control systems but also for simulation tools. It is now seeming necessary to develop a model that can record, find meaningful relationship and predict more holistic human interactions in buildings. Such model could create capacity for feedback and control with a level of intelligence. Fuzzy Logic Systems (FLSs) are known as robust tools in decision making and developing models in an efficient manner. Considering this capability, in this paper, FLSs is implemented to make a thermal comfort model in an educational building in the UK. Such implementation has an ability to respond to some identified desires of developers and performance assessors in addressing uncertainty in thermal comfort models. The results demonstrate the proposed method is practical to simulate the value of comfort level based on the input data